Real-time upper-body garment exchange

ABSTRACT

Methods and systems are disclosed for performing operations for transferring garments from one real-world object to another in real time. The operations comprise receiving a first video that includes a depiction of a first person wearing a first upper-body garment in a first pose and obtaining a second video that includes a depiction of a second person wearing a second upper-body garment in a second pose. A pose of the second person depicted in the second video is modified to match the first pose of the first person depicted in the first video. The operations comprise generating an upper-body segmentation of the second upper-body garment which the second person is wearing in the second video in the modified pose and replacing the first upper-body garment worn by the first person in the first video with the second upper-body garment based on the upper-body segmentation.

TECHNICAL FIELD

The present disclosure relates generally to providing augmented reality(AR) experiences using a messaging application.

BACKGROUND

Augmented-Reality (AR) is a modification of a virtual environment. Forexample, in Virtual Reality (VR), a user is completely immersed in avirtual world, whereas in AR, the user is immersed in a world wherevirtual objects are combined or superimposed on the real world. An ARsystem aims to generate and present virtual objects that interactrealistically with a real-world environment and with each other.Examples of AR applications can include single or multiple player videogames, instant messaging systems, and the like.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some nonlimiting examples areillustrated in the figures of the accompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples.

FIG. 2 is a diagrammatic representation of a messaging clientapplication, in accordance with some examples.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some examples.

FIG. 4 is a diagrammatic representation of a message, in accordance withsome examples.

FIG. 5 is a block diagram showing an example motion and appearancetransfer system, according to some examples.

FIGS. 6 and 7 are diagrammatic representations of outputs of the motionand appearance transfer system, in accordance with some examples.

FIG. 8 is a flowchart illustrating example operations of the motion andappearance transfer system, according to some examples.

FIG. 9 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples.

FIG. 10 is a block diagram showing a software architecture within whichexamples may be implemented.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative examples of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of various examples.It will be evident, however, to those skilled in the art, that examplesmay be practiced without these specific details. In general, well-knowninstruction instances, protocols, structures, and techniques are notnecessarily shown in detail.

Typically, VR and AR systems display images representing a given user bycapturing an image of the user and, in addition, obtaining a depth mapusing a depth sensor of the real-world human body depicted in the image.By processing the depth map and the image together, the VR and ARsystems can detect positioning of a user in the image and canappropriately modify the user or background in the images. While suchsystems work well, the need for a depth sensor limits the scope of theirapplications. This is because adding depth sensors to user devices forthe purpose of modifying images increases the overall cost andcomplexity of the devices, making them less attractive.

Certain systems do away with the need to use depth sensors to modifyimages. For example, certain systems allow users to replace a backgroundin a videoconference in which a face of the user is detected.Specifically, such systems can use specialized techniques that areoptimized for recognizing a face of a user to identify the background inthe images that depict the user’s face. These systems can then replaceonly those pixels that depict the background so that the real-worldbackground is replaced with an alternate background in the images.However, such systems are generally incapable of recognizing a wholebody of a user. As such, if the user is more than a threshold distancefrom the camera such that more than just the face of the user iscaptured by the camera, the replacement of the background with analternate background begins to fail. In such cases, the image quality isseverely impacted, and portions of the face and body of the user can beinadvertently removed by the system as the system falsely identifiessuch portions as belonging to the background rather than the foregroundof the images. Also, such systems fail to properly replace thebackground when more than one user is depicted in the image or videofeed. Because such systems are generally incapable of distinguishing awhole body of a user in an image from a background, these systems arealso unable to apply visual effects to certain portions of a user’sbody, such as articles of clothing, garments, or fashion accessories anditems (e.g., jewelry, handbags, purses, and so forth).

Some AR systems allow AR graphics to be added to an image or video toprovide engaging AR experiences. Such systems can receive the ARgraphics from a designer and can scale and position the AR graphicswithin the image or video. In order to improve the placement andpositioning of the AR graphics on a person depicted in the image orvideo, such systems detect a person depicted in the image or video andgenerate a rig representing bones of the person. This rig is then usedto adjust the AR graphics based on changes in movement to the rig. Whilesuch approaches generally work well, the need for generating a rig of aperson in real time to adjust AR graphics placement increases processingcomplexities and power and memory requirements. This makes such systemsinefficient or incapable of running on small-scale mobile deviceswithout sacrificing computing resources or processing speed. Also, therig only represents movement of skeletal or bone structures of a personin the image or video and does not take into account any sort ofexternal physical properties of the person, such as density, weight,skin attributes, and so forth. As such, any AR graphics in these systemscan be adjusted in scale and positioning but cannot be deformed based onother physical properties of the person. In addition, an AR graphicsdesigner typically needs to create a compatible rig for their AR graphicor AR fashion item.

The disclosed techniques improve the efficiency of using the electronicdevice by using a combination of machine learning techniques (e.g.,neural networks) to extract appearance and motion featuressimultaneously of a person depicted in one image and to render a newimage that depicts the person with the same motion but differentappearance features corresponding to a person depicted in a differentimage. By using a machine learning technique to extract the appearanceand motion features simultaneously, the disclosed techniques can applyone or more visual effects to the image or video in association with theperson depicted in the image or video in real time in a more efficientmanner and without the need for generating a rig or bone structures ofthe depicted obj ect.

In one example, a first client device of a first person can be used tocapture first and second images or videos of the first person and asecond person, respectively. The motion features of the first person canbe extracted from the captured first image or video and used to adjustmotion of the second person. Namely, a machine learning technique can beapplied to combine the appearance features of the second person with themotion features of the first person to render a new image or video thatdepicts the second person performing motion corresponding to the motionof the first person without changing the appearance of the first person.After motion of the second person is adjusted, an upper-body garmentsegmentation is applied to one or more upper-body garments worn by thesecond person. The upper-body garment segmentation of the one or moreupper-body garments worn by the second person is then used to replace anupper-body garment worn by the first person depicted in the first imageor video.

In some examples, lighting conditions of the first image or video thatdepicts the first person are used to adjust one or more colors of theone or more upper-body garments worn by the second person. This way,when the one or more upper-body garments of the second person areapplied and used to replace the upper-body garment worn by the firstperson in the first video, the lighting conditions of the first videoare preserved. This maintains the illusion that the upper-body garmentsof the second person are part of the real-world environment of the firstperson.

This simplifies the process of adding AR graphics to an image or videowhich significantly reduces design constraints and costs in generatingsuch AR graphics and decreases the amount of processing complexities andpower and memory requirements. This also improves the illusion of the ARgraphics being part of a real-world environment depicted in an image orvideo that depicts the object. This enables seamless and efficientaddition of AR graphics to an underlying image or video in real time onsmall-scale mobile devices. The disclosed techniques can be appliedexclusively or mostly on a mobile device without the need for the mobiledevice to send images/videos to a server. In other examples, thedisclosed techniques are applied exclusively or mostly on a remoteserver or can be divided between a mobile device and a server.

As a result, a realistic display can be provided that shows the userwearing an AR fashion item while changing a real-world pose or motion ofthe user based on a pose or motion of a different user. This improvesthe overall experience of the user in using the electronic device. Also,by providing such AR experiences without using a depth sensor, theoverall amount of system resources needed to accomplish a task isreduced. As used herein, “article of clothing,” “fashion item,” and“garment” are used interchangeably and should be understood to have thesame meaning.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications, including a messagingclient 104 and other external applications 109 (e.g., third-partyapplications). Each messaging client 104 is communicatively coupled toother instances of the messaging client 104 (e.g., hosted on respectiveother client devices 102), a messaging server system 108 and externalapp(s) servers 110 via a network 112 (e.g., the Internet). A messagingclient 104 can also communicate with locally-hosted third-partyapplications, such as external apps 109 using Application ProgrammingInterfaces (APIs).

A messaging client 104 is able to communicate and exchange data withother messaging clients 104 and with the messaging server system 108 viathe network 112. The data exchanged between messaging clients 104, andbetween a messaging client 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 112 to a particular messaging client 104. While certainfunctions of the messaging system 100 are described herein as beingperformed by either a messaging client 104 or by the messaging serversystem 108, the location of certain functionality either within themessaging client 104 or the messaging server system 108 may be a designchoice. For example, it may be technically preferable to initiallydeploy certain technology and functionality within the messaging serversystem 108 but to later migrate this technology and functionality to themessaging client 104 where a client device 102 has sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client 104. Such operations includetransmitting data to, receiving data from, and processing data generatedby the messaging client 104. This data may include message content,client device information, geolocation information, media augmentationand overlays, message content persistence conditions, social networkinformation, and live event information, as examples. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces (UIs) of the messaging client104.

Turning now specifically to the messaging server system 108, an APIserver 116 is coupled to, and provides a programmatic interface to,application servers 114. The application servers 114 are communicativelycoupled to a database server 120, which facilitates access to a database126 that stores data associated with messages processed by theapplication servers 114. Similarly, a web server 128 is coupled to theapplication servers 114 and provides web-based interfaces to theapplication servers 114. To this end, the web server 128 processesincoming network requests over the Hypertext Transfer Protocol (HTTP)and several other related protocols.

The API server 116 receives and transmits message data (e.g., commandsand message payloads) between the client device 102 and the applicationservers 114. Specifically, the API server 116 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client 104 in order to invoke functionality of theapplication servers 114. The API server 116 exposes various functionssupported by the application servers 114, including accountregistration; login functionality; the sending of messages, via theapplication servers 114, from a particular messaging client 104 toanother messaging client 104; the sending of media files (e.g., imagesor video) from a messaging client 104 to a messaging server 118, and forpossible access by another messaging client 104; the settings of acollection of media data (e.g., story); the retrieval of a list offriends of a user of a client device 102; the retrieval of suchcollections; the retrieval of messages and content; the addition anddeletion of entities (e.g., friends) to an entity graph (e.g., a socialgraph); the location of friends within a social graph; and opening anapplication event (e.g., relating to the messaging client 104).

The application servers 114 host a number of server applications andsubsystems, including, for example, a messaging server 118, an imageprocessing server 122, and a social network server 124. The messagingserver 118 implements a number of message processing technologies andfunctions, particularly related to the aggregation and other processingof content (e.g., textual and multimedia content) included in messagesreceived from multiple instances of the messaging client 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available to themessaging client 104. Other processor- and memory-intensive processingof data may also be performed server-side by the messaging server 118,in view of the hardware requirements for such processing.

The application servers 114 also include an image processing server 122that is dedicated to performing various image processing operations,typically with respect to images or video within the payload of amessage sent from or received at the messaging server 118.

Image processing server 122 is used to implement scan functionality ofthe augmentation system 208 (shown in FIG. 2 ). Scan functionalityincludes activating and providing one or more AR experiences on a clientdevice 102 when an image is captured by the client device 102.Specifically, the messaging client 104 on the client device 102 can beused to activate a camera. The camera displays one or more real-timeimages or a video to a user along with one or more icons or identifiersof one or more AR experiences. The user can select a given one of theidentifiers to launch the corresponding AR experience or perform adesired image modification (e.g., replacing a garment being worn by auser in a video or recoloring the garment worn by the user in the videoor modifying the garment based on a gesture performed by the user).

The social network server 124 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server 118. To this end, the social network server 124maintains and accesses an entity graph 308 (as shown in FIG. 3 ) withinthe database 126. Examples of functions and services supported by thesocial network server 124 include the identification of other users ofthe messaging system 100 with which a particular user has relationshipsor is “following” and also the identification of other entities andinterests of a particular user.

Returning to the messaging client 104, features and functions of anexternal resource (e.g., a third-party application 109 or applet) aremade available to a user via an interface of the messaging client 104.The messaging client 104 receives a user selection of an option tolaunch or access features of an external resource (e.g., a third-partyresource), such as external apps 109. The external resource may be athird-party application (external apps 109) installed on the clientdevice 102 (e.g., a “native app”), or a small-scale version of thethird-party application (e.g., an “applet”) that is hosted on the clientdevice 102 or remote of the client device 102 (e.g., on third-partyservers 110). The small-scale version of the third-party applicationincludes a subset of features and functions of the third-partyapplication (e.g., the full-scale, native version of the third-partystandalone application) and is implemented using a markup-languagedocument. In one example, the small-scale version of the third-partyapplication (e.g., an “applet”) is a web-based, markup-language versionof the third-party application and is embedded in the messaging client104. In addition to using markup-language documents (e.g., a .*ml file),an applet may incorporate a scripting language (e.g., a .*js file or a.json file) and a style sheet (e.g., a .*ss file).

In response to receiving a user selection of the option to launch oraccess features of the external resource (external app 109), themessaging client 104 determines whether the selected external resourceis a web-based external resource or a locally-installed externalapplication. In some cases, external applications 109 that are locallyinstalled on the client device 102 can be launched independently of andseparately from the messaging client 104, such as by selecting an icon,corresponding to the external application 109, on a home screen of theclient device 102. Small-scale versions of such external applicationscan be launched or accessed via the messaging client 104 and, in someexamples, no or limited portions of the small-scale external applicationcan be accessed outside of the messaging client 104. The small-scaleexternal application can be launched by the messaging client 104receiving, from an external app(s) server 110, a markup-languagedocument associated with the small-scale external application andprocessing such a document.

In response to determining that the external resource is alocally-installed external application 109, the messaging client 104instructs the client device 102 to launch the external application 109by executing locally-stored code corresponding to the externalapplication 109. In response to determining that the external resourceis a web-based resource, the messaging client 104 communicates with theexternal app(s) servers 110 to obtain a markup-language documentcorresponding to the selected resource. The messaging client 104 thenprocesses the obtained markup-language document to present the web-basedexternal resource within a user interface of the messaging client 104.

The messaging client 104 can notify a user of the client device 102, orother users related to such a user (e.g., “friends”), of activity takingplace in one or more external resources. For example, the messagingclient 104 can provide participants in a conversation (e.g., a chatsession) in the messaging client 104 with notifications relating to thecurrent or recent use of an external resource by one or more members ofa group of users. One or more users can be invited to join in an activeexternal resource or to launch a recently-used but currently inactive(in the group of friends) external resource. The external resource canprovide participants in a conversation, each using a respectivemessaging client 104, with the ability to share an item, status, state,or location in an external resource with one or more members of a groupof users into a chat session. The shared item may be an interactive chatcard with which members of the chat can interact, for example, to launchthe corresponding external resource, view specific information withinthe external resource, or take the member of the chat to a specificlocation or state within the external resource. Within a given externalresource, response messages can be sent to users on the messaging client104. The external resource can selectively include different media itemsin the responses, based on a current context of the external resource.

The messaging client 104 can present a list of the available externalresources (e.g., third-party or external applications 109 or applets) toa user to launch or access a given external resource. This list can bepresented in a context-sensitive menu. For example, the iconsrepresenting different ones of the external application 109 (or applets)can vary based on how the menu is launched by the user (e.g., from aconversation interface or from a non-conversation interface).

In an example, the messaging client 104 enables a user to launch an ARexperience in which a first person (e.g., the user of the messagingclient 104) is depicted as wearing one or more upper-body garments wornby a second person depicted in another image. Particularly, themessaging client 104 implemented on a first client device of a firstperson can be used to capture an image or video of a second person. Themessaging client 104 implemented on the first client device of the firstperson can be used to also capture an image or video of the firstperson. The motion features of the first person can be extracted fromthe captured image or video. The messaging client 104 on the firstclient device can extract appearance features of the second person fromthe image or video. The messaging client 104 can apply a machinelearning technique to combine the appearance features of the secondperson with the motion features of the first person to render a newimage or video that depicts the second person performing motioncorresponding to the motion of the first person without changing theappearance of the second person.

In an example, this results in modifying the pose of the second personto match the pose of the first person without changing the appearance ofthe second person (e.g., upper-body clothing worn by the second person).A segmentation of the second person’s upper-body garments can beperformed after modifying the pose or motion of the second person. Thesegmentation is used to obtain pixel values and positions of theupper-body garments worn by the second person performing the pose of thefirst person. These pixels values and positions are copied using a maskand used to modify corresponding pixel values and positions of the firstperson.

In some examples, lighting conditions of the image or video that depictsthe first person can be obtained. The upper-body garments worn by thesecond person can be extracted based on the upper-body segmentation ofthe second person. Namely, after the pose or motion of the second personis changed, the upper-body garments worn by the second person in thechanged pose or motion are extracted. The lighting conditions can beused to change one or more colors of the extracted upper-body garments.The upper-body garments with the changed one or more colors can then beused to modify the upper-body garment worn by the first person depictedin the image or video. For example, a sweater worn by the second personcan be rendered as being worn by the first person without changinganother article of clothing worn by the first person (e.g., jeans orshorts).

This results in the appearance of the first person changing from wearingone upper-body garment or article of clothing to wearing anotherupper-body garment or article of clothing that is being worn by thesecond person. In some implementations, similar techniques can beapplied to replace the upper-body garment worn by the second person withthe upper-body garment worn by the first person. In this way, the firstperson can view how the first person looks wearing a real-worldupper-body article of clothing or fashion item of a second person inreal time. Article of clothing, garment, or fashion item can include ashirt, pants, skirt, dress, jewelry, purse, furniture item, householditem, eyewear, eyeglasses, AR logos, AR emblems, purse, pants, shorts,skirts, jackets, t-shirts, blouses, glasses, jewelry, earrings, bunnyears, a hat, ear muffs, or any other suitable item or object. Furtherdetails of this AR experience are discussed below in connection with themotion and appearance transfer system 224 of FIG. 5 .

System Architecture

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to some examples. Specifically, themessaging system 100 is shown to comprise the messaging client 104 andthe application servers 114. The messaging system 100 embodies a numberof subsystems, which are supported on the client side by the messagingclient 104 and on the sever side by the application servers 114. Thesesubsystems include, for example, an ephemeral timer system 202, acollection management system 204, an augmentation system 208, a mapsystem 210, a game system 212, and an external resource system 220.

The ephemeral timer system 202 is responsible for enforcing thetemporary or time-limited access to content by the messaging client 104and the messaging server 118. The ephemeral timer system 202incorporates a number of timers that, based on duration and displayparameters associated with a message, or collection of messages (e.g., astory), selectively enable access (e.g., for presentation and display)to messages and associated content via the messaging client 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managing sets orcollections of media (e.g., collections of text, image video, and audiodata). A collection of content (e.g., messages, including images, video,text, and audio) may be organized into an “event gallery” or an “eventstory.” Such a collection may be made available for a specified timeperiod, such as the duration of an event to which the content relates.For example, content relating to a music concert may be made availableas a “story” for the duration of that music concert. The collectionmanagement system 204 may also be responsible for publishing an iconthat provides notification of the existence of a particular collectionto the user interface of the messaging client 104.

The collection management system 204 further includes a curationinterface 206 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface206 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certain examples,compensation may be paid to a user for the inclusion of user-generatedcontent into a collection. In such cases, the collection managementsystem 204 operates to automatically make payments to such users for theuse of their content.

The augmentation system 208 provides various functions that enable auser to augment (e.g., annotate or otherwise modify or edit) mediacontent associated with a message. For example, the augmentation system208 provides functions related to the generation and publishing of mediaoverlays for messages processed by the messaging system 100. Theaugmentation system 208 operatively supplies a media overlay oraugmentation (e.g., an image filter) to the messaging client 104 basedon a geolocation of the client device 102. In another example, theaugmentation system 208 operatively supplies a media overlay to themessaging client 104 based on other information, such as social networkinformation of the user of the client device 102. A media overlay mayinclude audio and visual content and visual effects. Examples of audioand visual content include pictures, texts, logos, animations, and soundeffects. An example of a visual effect includes color overlaying. Theaudio and visual content or the visual effects can be applied to a mediacontent item (e.g., a photo) at the client device 102. For example, themedia overlay may include text, a graphical element, or an image thatcan be overlaid on top of a photograph taken by the client device 102.In another example, the media overlay includes an identification of alocation overlay (e.g., Venice beach), a name of a live event, or a nameof a merchant overlay (e.g., Beach Coffee House). In another example,the augmentation system 208 uses the geolocation of the client device102 to identify a media overlay that includes the name of a merchant atthe geolocation of the client device 102. The media overlay may includeother indicia associated with the merchant. The media overlays may bestored in the database 126 and accessed through the database server 120.

In some examples, the augmentation system 208 provides a user-basedpublication platform that enables users to select a geolocation on a mapand upload content associated with the selected geolocation. The usermay also specify circumstances under which a particular media overlayshould be offered to other users. The augmentation system 208 generatesa media overlay that includes the uploaded content and associates theuploaded content with the selected geolocation.

In other examples, the augmentation system 208 provides a merchant-basedpublication platform that enables merchants to select a particular mediaoverlay associated with a geolocation via a bidding process. Forexample, the augmentation system 208 associates the media overlay of thehighest bidding merchant with a corresponding geolocation for apredefined amount of time. The augmentation system 208 communicates withthe image processing server 122 to obtain AR experiences and presentsidentifiers of such experiences in one or more user interfaces (e.g., asicons over a real-time image or video or as thumbnails or icons ininterfaces dedicated for presented identifiers of AR experiences). Oncean AR experience is selected, one or more images, videos, or ARgraphical elements are retrieved and presented as an overlay on top ofthe images or video captured by the client device 102. In some cases,the camera is switched to a front-facing view (e.g., the front-facingcamera of the client device 102 is activated in response to activationof a particular AR experience) and the images from the front-facingcamera of the client device 102 start being displayed on the clientdevice 102 instead of the rear-facing camera of the client device 102.The one or more images, videos, or AR graphical elements are retrievedand presented as an overlay on top of the images that are captured anddisplayed by the front-facing camera of the client device 102.

In other examples, the augmentation system 208 is able to communicateand exchange data with another augmentation system 208 on another clientdevice 102 and with the server via the network 112. The data exchangedcan include a session identifier that identifies the shared AR session,a transformation between a first client device 102 and a second clientdevice 102 (e.g., a plurality of client devices 102 include the firstand second devices) that is used to align the shared AR session to acommon point of origin, a common coordinate frame, functions (e.g.,commands to invoke functions), and other payload data (e.g., text,audio, video or other multimedia data).

The augmentation system 208 sends the transformation to the secondclient device 102 so that the second client device102 can adjust the ARcoordinate system based on the transformation. In this way, the firstand second client devices 102 synch up their coordinate systems andframes for displaying content in the AR session. Specifically, theaugmentation system 208 computes the point of origin of the secondclient device 102 in the coordinate system of the first client device102. The augmentation system 208 can then determine an offset in thecoordinate system of the second client device 102 based on the positionof the point of origin from the perspective of the second client device102 in the coordinate system of the second client device 102. Thisoffset is used to generate the transformation so that the second clientdevice 102 generates AR content according to a common coordinate systemor frame as the first client device 102.

The augmentation system 208 can communicate with the client device 102to establish individual or shared AR sessions. The augmentation system208 can also be coupled to the messaging server 118 to establish anelectronic group communication session (e.g., group chat, instantmessaging) for the client devices 102 in a shared AR session. Theelectronic group communication session can be associated with a sessionidentifier provided by the client devices 102 to gain access to theelectronic group communication session and to the shared AR session. Inone example, the client devices 102 first gain access to the electronicgroup communication session and then obtain the session identifier inthe electronic group communication session that allows the clientdevices 102 to access the shared AR session. In some examples, theclient devices 102 are able to access the shared AR session without aidor communication with the augmentation system 208 in the applicationservers 114.

The map system 210 provides various geographic location functions andsupports the presentation of map-based media content and messages by themessaging client 104. For example, the map system 210 enables thedisplay of user icons or avatars (e.g., stored in profile data 316) on amap to indicate a current or past location of “friends” of a user, aswell as media content (e.g., collections of messages includingphotographs and videos) generated by such friends, within the context ofa map. For example, a message posted by a user to the messaging system100 from a specific geographic location may be displayed within thecontext of a map at that particular location to “friends” of a specificuser on a map interface of the messaging client 104. A user canfurthermore share his or her location and status information (e.g.,using an appropriate status avatar) with other users of the messagingsystem 100 via the messaging client 104, with this location and statusinformation being similarly displayed within the context of a mapinterface of the messaging client 104 to selected users.

The game system 212 provides various gaming functions within the contextof the messaging client 104. The messaging client 104 provides a gameinterface providing a list of available games (e.g., web-based games orweb-based applications) that can be launched by a user within thecontext of the messaging client 104 and played with other users of themessaging system 100. The messaging system 100 further enables aparticular user to invite other users to participate in the play of aspecific game, by issuing invitations to such other users from themessaging client 104. The messaging client 104 also supports both voiceand text messaging (e.g., chats) within the context of gameplay,provides a leaderboard for the games, and also supports the provision ofin-game rewards (e.g., coins and items).

The external resource system 220 provides an interface for the messagingclient 104 to communicate with external app(s) servers 110 to launch oraccess external resources. Each external resource (apps) server 110hosts, for example, a markup language (e.g., HTML5) based application orsmall-scale version of an external application (e.g., game, utility,payment, or ride-sharing application that is external to the messagingclient 104). The messaging client 104 may launch a web-based resource(e.g., application) by accessing the HTML5 file from the externalresource (apps) servers 110 associated with the web-based resource. Incertain examples, applications hosted by external resource servers 110are programmed in JavaScript leveraging a Software Development Kit (SDK)provided by the messaging server 118. The SDK includes APIs withfunctions that can be called or invoked by the web-based application. Incertain examples, the messaging server 118 includes a JavaScript librarythat provides a given third-party resource access to certain user dataof the messaging client 104. HTML5 is used as an example technology forprogramming games, but applications and resources programmed based onother technologies can be used.

In order to integrate the functions of the SDK into the web-basedresource, the SDK is downloaded by an external resource (apps) server110 from the messaging server 118 or is otherwise received by theexternal resource (apps) server 110. Once downloaded or received, theSDK is included as part of the application code of a web-based externalresource. The code of the web-based resource can then call or invokecertain functions of the SDK to integrate features of the messagingclient 104 into the web-based resource.

The SDK stored on the messaging server 118 effectively provides thebridge between an external resource (e.g., third-party or externalapplications 109 or applets) and the messaging client 104. This providesthe user with a seamless experience of communicating with other users onthe messaging client 104, while also preserving the look and feel of themessaging client 104. To bridge communications between an externalresource and a messaging client 104, in certain examples, the SDKfacilitates communication between external resource servers 110 and themessaging client 104. In certain examples, a WebViewJavaScriptBridgerunning on a client device 102 establishes two one-way communicationchannels between an external resource and the messaging client 104.Messages are sent between the external resource and the messaging client104 via these communication channels asynchronously. Each SDK functioninvocation is sent as a message and callback. Each SDK function isimplemented by constructing a unique callback identifier and sending amessage with that callback identifier.

By using the SDK, not all information from the messaging client 104 isshared with external resource servers 110. The SDK limits whichinformation is shared based on the needs of the external resource. Incertain examples, each external resource server 110 provides an HTML5file corresponding to the web-based external resource to the messagingserver 118. The messaging server 118 can add a visual representation(such as a box art or other graphic) of the web-based external resourcein the messaging client 104. Once the user selects the visualrepresentation or instructs the messaging client 104 through a GUI ofthe messaging client 104 to access features of the web-based externalresource, the messaging client 104 obtains the HTML5 file andinstantiates the resources necessary to access the features of theweb-based external resource.

The messaging client 104 presents a graphical user interface (e.g., alanding page or title screen) for an external resource. During, before,or after presenting the landing page or title screen, the messagingclient 104 determines whether the launched external resource has beenpreviously authorized to access user data of the messaging client 104.In response to determining that the launched external resource has beenpreviously authorized to access user data of the messaging client 104,the messaging client 104 presents another graphical user interface ofthe external resource that includes functions and features of theexternal resource. In response to determining that the launched externalresource has not been previously authorized to access user data of themessaging client 104, after a threshold period of time (e.g., 3 seconds)of displaying the landing page or title screen of the external resource,the messaging client 104 slides up (e.g., animates a menu as surfacingfrom a bottom of the screen to a middle of or other portion of thescreen) a menu for authorizing the external resource to access the userdata. The menu identifies the type of user data that the externalresource will be authorized to use. In response to receiving a userselection of an accept option, the messaging client 104 adds theexternal resource to a list of authorized external resources and allowsthe external resource to access user data from the messaging client 104.In some examples, the external resource is authorized by the messagingclient 104 to access the user data in accordance with an OAuth 2framework.

The messaging client 104 controls the type of user data that is sharedwith external resources based on the type of external resource beingauthorized. For example, external resources that include full-scaleexternal applications (e.g., a third-party or external application 109)are provided with access to a first type of user data (e.g., onlytwo-dimensional (2D) avatars of users with or without different avatarcharacteristics). As another example, external resources that includesmall-scale versions of external applications (e.g., web-based versionsof third-party applications) are provided with access to a second typeof user data (e.g., payment information, 2D avatars of users, 3D avatarsof users, and avatars with various avatar characteristics). Avatarcharacteristics include different ways to customize a look and feel ofan avatar, such as different poses, facial features, clothing, and soforth.

A motion and appearance transfer system 224 enables a user to launch anAR experience in which the user (e.g., a first person) is depicted aswearing an upper-body garment worn by a second person depicted inanother image. An illustrative implementation of the motion andappearance transfer system 224 is shown and described in connection withFIG. 5 below.

Specifically, the motion and appearance transfer system 224 is acomponent that can be accessed by an AR/VR application implemented onthe client device 102. The AR/VR application uses an RGB camera tocapture a monocular image of a user. The AR/VR application appliesvarious trained machine learning techniques on the captured image of theuser to generate appearance and motion features representing the userdepicted in the image or video and to apply one or more AR visualeffects to the captured image or video based on motion and/or appearancefeatures (e.g., 3D pose, 3D body mesh, full body, upper-body orwhole-body segmentation, set of dense keypoints, texture, color, and/orgarment segmentation) of a different user depicted in another image orvideo. In some implementations, the AR/VR application continuouslycaptures images of the user and updates the motion and/or appearancefeatures in real time or periodically to continuously or periodicallyupdate the applied one or more visual effects. This allows the user tomove around in the real world and see the one or more visual effects(e.g., the motion of another user applied to the appearance of the user)update in real time.

Data Architecture

FIG. 3 is a schematic diagram illustrating data structures 300, whichmay be stored in the database 126 of the messaging server system 108,according to certain examples. While the content of the database 126 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 126 includes message data stored within a message table302. This message data includes, for any particular one message, atleast message sender data, message recipient (or receiver) data, and apayload. Further details regarding information that may be included in amessage, and included within the message data stored in the messagetable 302, are described below with reference to FIG. 4 .

An entity table 306 stores entity data and is linked (e.g.,referentially) to an entity graph 308 and profile data 316. Entities forwhich records are maintained within the entity table 306 may includeindividuals, corporate entities, organizations, objects, places, events,and so forth. Regardless of entity type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 308 stores information regarding relationships andassociations between entities. Such relationships may be social,professional (e.g., work at a common corporation or organization),interest-based, or activity-based, merely for example.

The profile data 316 stores multiple types of profile data about aparticular entity. The profile data 316 may be selectively used andpresented to other users of the messaging system 100, based on privacysettings specified by a particular entity. Where the entity is anindividual, the profile data 316 includes, for example, a user name,telephone number, address, and settings (e.g., notification and privacysettings), as well as a user-selected avatar representation (orcollection of such avatar representations). A particular user may thenselectively include one or more of these avatar representations withinthe content of messages communicated via the messaging system 100 and onmap interfaces displayed by messaging clients 104 to other users. Thecollection of avatar representations may include “status avatars,” whichpresent a graphical representation of a status or activity that the usermay select to communicate at a particular time.

Where the entity is a group, the profile data 316 for the group maysimilarly include one or more avatar representations associated with thegroup, in addition to the group name, members, and various settings(e.g., notifications) for the relevant group.

The database 126 also stores augmentation data, such as overlays orfilters, in an augmentation table 310. The augmentation data isassociated with and applied to videos (for which data is stored in avideo table 304) and images (for which data is stored in an image table312).

The database 126 can also store data pertaining to individual and sharedAR sessions. This data can include data communicated between an ARsession client controller of a first client device 102 and another ARsession client controller of a second client device 102, and datacommunicated between the AR session client controller and theaugmentation system 208. Data can include data used to establish thecommon coordinate frame of the shared AR scene, the transformationbetween the devices, the session identifier, images depicting a body,skeletal joint positions, wrist joint positions, feet, and so forth.

Filters, in one example, are overlays that are displayed as overlaid onan image or video during presentation to a recipient user. Filters maybe of various types, including user-selected filters from a set offilters presented to a sending user by the messaging client 104 when thesending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client 104, based ongeolocation information determined by a Global Positioning System (GPS)unit of the client device 102.

Another type of filter is a data filter, which may be selectivelypresented to a sending user by the messaging client 104, based on otherinputs or information gathered by the client device 102 during themessage creation process. Examples of data filters include currenttemperature at a specific location, a current speed at which a sendinguser is traveling, battery life for a client device 102, or the currenttime.

Other augmentation data that may be stored within the image table 312includes AR content items (e.g., corresponding to applying ARexperiences). An AR content item or AR item may be a real-time specialeffect and sound that may be added to an image or a video.

As described above, augmentation data includes AR content items,overlays, image transformations, AR images, AR logos or emblems, andsimilar terms that refer to modifications that may be applied to imagedata (e.g., videos or images). This includes real-time modifications,which modify an image as it is captured using device sensors (e.g., oneor multiple cameras) of a client device 102 and then displayed on ascreen of the client device 102 with the modifications. This alsoincludes modifications to stored content, such as video clips in agallery that may be modified. For example, in a client device 102 withaccess to multiple AR content items, a user can use a single video clipwith multiple AR content items to see how the different AR content itemswill modify the stored clip. For example, multiple AR content items thatapply different pseudorandom movement models can be applied to the samecontent by selecting different AR content items for the content.Similarly, real-time video capture may be used with an illustratedmodification to show how video images currently being captured bysensors of a client device 102 would modify the captured data. Such datamay simply be displayed on the screen and not stored in memory, or thecontent captured by the device sensors may be recorded and stored inmemory with or without the modifications (or both). In some systems, apreview feature can show how different AR content items will look withindifferent windows in a display at the same time. This can, for example,enable multiple windows with different pseudorandom animations to beviewed on a display at the same time.

Data and various systems using AR content items or other such transformsystems to modify content using this data can thus involve detection ofreal-world objects (e.g., faces, hands, bodies, cats, dogs, surfaces,objects, etc.), tracking of such objects as they leave, enter, and movearound the field of view in video frames, and the modification ortransformation of such objects as they are tracked. In various examples,different methods for achieving such transformations may be used. Someexamples may involve generating a 3D mesh model of the object or objectsand using transformations and animated textures of the model within thevideo to achieve the transformation. In other examples, tracking ofpoints on an object may be used to place an image or texture (which maybe 2D or 3D) at the tracked position. In still further examples, neuralnetwork analysis of video frames may be used to place images, models, ortextures in content (e.g., images or frames of video). AR content itemsor elements thus refer both to the images, models, and textures used tocreate transformations in content, as well as to additional modeling andanalysis information needed to achieve such transformations with objectdetection, tracking, and placement.

Real-time video processing can be performed with any kind of video data(e.g., video streams, video files, etc.) saved in a memory of acomputerized system of any kind. For example, a user can load videofiles and save them in a memory of a device or can generate a videostream using sensors of the device. Additionally, any objects can beprocessed using a computer animation model, such as a human’s face andparts of a human body, animals, or non-living things such as chairs,cars, or other objects.

In some examples, when a particular modification is selected along withcontent to be transformed, elements to be transformed are identified bythe computing device and then detected and tracked if they are presentin the frames of the video. The elements of the object are modifiedaccording to the request for modification, thus transforming the framesof the video stream. Transformation of frames of a video stream can beperformed by different methods for different kinds of transformation.For example, for transformations of frames mostly referring to changingforms of an object’s elements, characteristic points for each element ofan object are calculated (e.g., using an Active Shape Model (ASM) orother known methods). Then, a mesh based on the characteristic points isgenerated for each of the at least one element of the object. This meshis used in the following stage of tracking the elements of the object inthe video stream. In the process of tracking, the mentioned mesh foreach element is aligned with a position of each element. Then,additional points are generated on the mesh. A first set of first pointsis generated for each element based on a request for modification, and aset of second points is generated for each element based on the set offirst points and the request for modification. Then, the frames of thevideo stream can be transformed by modifying the elements of the objecton the basis of the sets of first and second points and the mesh. Insuch method, a background of the modified object can be changed ordistorted as well by tracking and modifying the background.

In some examples, transformations changing some areas of an object usingits elements can be performed by calculating characteristic points foreach element of an object and generating a mesh based on the calculatedcharacteristic points. Points are generated on the mesh and then variousareas based on the points are generated. The elements of the object arethen tracked by aligning the area for each element with a position foreach of the at least one element, and properties of the areas can bemodified based on the request for modification, thus transforming theframes of the video stream. Depending on the specific request formodification, properties of the mentioned areas can be transformed indifferent ways. Such modifications may involve changing color of areas;removing at least some part of areas from the frames of the videostream; including one or more new objects into areas which are based ona request for modification; and modifying or distorting the elements ofan area or object. In various examples, any combination of suchmodifications or other similar modifications may be used. For certainmodels to be animated, some characteristic points can be selected ascontrol points to be used in determining the entire state-space ofoptions for the model animation.

In some examples of a computer animation model to transform image datausing body/person detection, the body/person is detected on an imagewith use of a specific body/person detection algorithm (e.g., 3D humanpose estimation and mesh reconstruction processes). Then, an ASMalgorithm is applied to the body/person region of an image to detectbody/person feature reference points.

Other methods and algorithms suitable for body/person detection can beused. For example, in some examples, features are located using alandmark, which represents a distinguishable point present in most ofthe images under consideration. For body/person landmarks, for example,the location of the left arm may be used. If an initial landmark is notidentifiable, secondary landmarks may be used. Such landmarkidentification procedures may be used for any such objects. In someexamples, a set of landmarks forms a shape. Shapes can be represented asvectors using the coordinates of the points in the shape. One shape isaligned to another with a similarity transform (allowing translation,scaling, and rotation) that minimizes the average Euclidean distancebetween shape points. The mean shape is the mean of the aligned trainingshapes.

In some examples, a search is started for landmarks from the mean shapealigned to the position and size of the body/person determined by aglobal body/person detector. Such a search then repeats the steps ofsuggesting a tentative shape by adjusting the locations of shape pointsby template matching of the image texture around each point and thenconforming the tentative shape to a global shape model until convergenceoccurs. In some systems, individual template matches are unreliable, andthe shape model pools the results of the weak template matches to form astronger overall classifier. The entire search is repeated at each levelin an image pyramid, from coarse to fine resolution.

A transformation system can capture an image or video stream on a clientdevice (e.g., the client device 102) and perform complex imagemanipulations locally on the client device 102 while maintaining asuitable user experience, computation time, and power consumption. Thecomplex image manipulations may include size and shape changes, emotiontransfers (e.g., changing a face from a frown to a smile), statetransfers (e.g., aging a subject, reducing apparent age, changinggender), style transfers, graphical element application, 3D human poseestimation, 3D body mesh reconstruction, and any other suitable image orvideo manipulation implemented by a convolutional neural network thathas been configured to execute efficiently on the client device 102.

In some examples, a computer animation model to transform image data canbe used by a system where a user may capture an image or video stream ofthe user (e.g., a selfie) using a client device 102 having a neuralnetwork operating as part of a messaging client 104 operating on theclient device 102. The transformation system operating within themessaging client 104 determines the presence of a body/person within theimage or video stream and provides modification icons associated with acomputer animation model to transform image data, or the computeranimation model can be present as associated with an interface describedherein. The modification icons include changes that may be the basis formodifying the user’s body/person within the image or video stream aspart of the modification operation. Once a modification icon isselected, the transformation system initiates a process to convert theimage of the user to reflect the selected modification icon (e.g.,generate a smiling face on the user). A modified image or video streammay be presented in a graphical user interface displayed on the clientdevice 102 as soon as the image or video stream is captured and aspecified modification is selected. The transformation system mayimplement a complex convolutional neural network on a portion of theimage or video stream to generate and apply the selected modification.That is, the user may capture the image or video stream and be presentedwith a modified result in real-time or near real-time once amodification icon has been selected. Further, the modification may bepersistent while the video stream is being captured and the selectedmodification icon remains toggled. Machine-taught neural networks may beused to enable such modifications.

The graphical user interface, presenting the modification performed bythe transformation system, may supply the user with additionalinteraction options. Such options may be based on the interface used toinitiate the content capture and selection of a particular computeranimation model (e.g., initiation from a content creator userinterface). In various examples, a modification may be persistent afteran initial selection of a modification icon. The user may toggle themodification on or off by tapping or otherwise selecting the body/personbeing modified by the transformation system and store it for laterviewing or browse to other areas of the imaging application. Wheremultiple faces are modified by the transformation system, the user maytoggle the modification on or off globally by tapping or selecting asingle body/person modified and displayed within a graphical userinterface. In some examples, individual bodies/persons, among a group ofmultiple bodies/persons, may be individually modified, or suchmodifications may be individually toggled by tapping or selecting theindividual body/person or a series of individual bodies/personsdisplayed within the graphical user interface.

A story table 314 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 306). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client 104 may include an icon that is user-selectableto enable a sending user to add specific content to his or her personalstory.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client 104, to contribute content to aparticular live story. The live story may be identified to the user bythe messaging client 104, based on his or her location. The end resultis a “live story” told from a community perspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some examples, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

As mentioned above, the video table 304 stores video data that, in oneexample, is associated with messages for which records are maintainedwithin the message table 302. Similarly, the image table 312 storesimage data associated with messages for which message data is stored inthe entity table 306. The entity table 306 may associate variousaugmentations from the augmentation table 310 with various images andvideos stored in the image table 312 and the video table 304.

Trained machine learning technique(s) 307 stores parameters that havebeen trained during training of the motion and appearance transfersystem 224. For example, trained machine learning techniques 307 storesthe trained parameters of one or more neural network machine learningtechniques.

DATA COMMUNICATIONS ARCHITECTURE

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some examples, generated by a messaging client 104 forcommunication to a further messaging client 104 or the messaging server118. The content of a particular message 400 is used to populate themessage table 302 stored within the database 126, accessible by themessaging server 118. Similarly, the content of a message 400 is storedin memory as “in-transit” or “in-flight” data of the client device 102or the application servers 114. A message 400 is shown to include thefollowing example components:

-   message identifier 402: a unique identifier that identifies the    message 400.-   message text payload 404: text, to be generated by a user via a user    interface of the client device 102, and that is included in the    message 400.-   message image payload 406: image data, captured by a camera    component of a client device 102 or retrieved from a memory    component of a client device 102, and that is included in the    message 400. Image data for a sent or received message 400 may be    stored in the image table 312.-   message video payload 408: video data, captured by a camera    component or retrieved from a memory component of the client device    102, and that is included in the message 400. Video data for a sent    or received message 400 may be stored in the video table 304.-   message audio payload 410: audio data, captured by a microphone or    retrieved from a memory component of the client device 102, and that    is included in the message 400.-   message augmentation data 412: augmentation data (e.g., filters,    stickers, or other annotations or enhancements) that represents    augmentations to be applied to message image payload 406, message    video payload 408, or message audio payload 410 of the message 400.    Augmentation data for a sent or received message 400 may be stored    in the augmentation table 310.-   message duration parameter 414: parameter value indicating, in    seconds, the amount of time for which content of the message (e.g.,    the message image payload 406, message video payload 408, message    audio payload 410) is to be presented or made accessible to a user    via the messaging client 104.-   message geolocation parameter 416: geolocation data (e.g.,    latitudinal and longitudinal coordinates) associated with the    content payload of the message. Multiple message geolocation    parameter 416 values may be included in the payload, each of these    parameter values being associated with respect to content items    included in the content (e.g., a specific image within the message    image payload 406, or a specific video in the message video payload    408).-   message story identifier 418: identifier values identifying one or    more content collections (e.g., “stories” identified in the story    table 314) with which a particular content item in the message image    payload 406 of the message 400 is associated. For example, multiple    images within the message image payload 406 may each be associated    with multiple content collections using identifier values.-   message tag 420: each message 400 may be tagged with multiple tags,    each of which is indicative of the subject matter of content    included in the message payload. For example, where a particular    image included in the message image payload 406 depicts an animal    (e.g., a lion), a tag value may be included within the message tag    420 that is indicative of the relevant animal. Tag values may be    generated manually, based on user input, or may be automatically    generated using, for example, image recognition.-   message sender identifier 422: an identifier (e.g., a messaging    system identifier, email address, or device identifier) indicative    of a user of the client device 102 on which the message 400 was    generated and from which the message 400 was sent.-   message receiver identifier 424: an identifier (e.g., a messaging    system identifier, email address, or device identifier) indicative    of a user of the client device 102 to which the message 400 is    addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 312.Similarly, values within the message video payload 408 may point to datastored within a video table 304, values stored within the messageaugmentation data 412 may point to data stored in an augmentation table310, values stored within the message story identifier 418 may point todata stored in a story table 314, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within an entity table 306.

Motion and Appearance Transfer System

FIG. 5 is a block diagram showing an example motion and appearancetransfer system 224, according to some examples. Motion and appearancetransfer system 224 includes a set of components 510 that operate on aset of input data (e.g., one or more monocular images or videosdepicting a real-world object, such as a person or training data). Theset of input data is obtained from one or more database(s) (FIG. 3 )during the training phases and is obtained from an RGB camera of aclient device 102 when an AR/VR application is being used, such as by amessaging client 104. Motion and appearance transfer system 224 includesmultiple machine learning techniques (e.g., a first machine learningtechnique module and a second machine learning technique module).Particularly, the motion and appearance transfer system 224 includes anappearance extraction module 512, a motion extraction module 514, an AReffect module 519, an image rendering module 518, a 3D body trackingmodule 513 and an upper-body segmentation module 515.

The motion and appearance transfer system 224 enables an AR experiencein which a first person can be depicted as wearing an upper-body garmentworn by a second person. For example, the motion and appearance transfersystem 224 enables the first person to capture a first video or image ofthe first person, such as using a front-facing camera of a client device102 of the first person. The first video or image may depict the firstperson performing a first pose or motion and wearing a first upper-bodygarment (e.g., a t-shirt, sweater, jewelry, and so forth). The motionand appearance transfer system 224 captures the first image or video ofthe first person, such as by activating a front-facing or rear-facingimage capture device (camera) of the client device 102 of the firstperson.

The motion and appearance transfer system 224 enables the first personto capture or receive a second video or image of a second person, suchas using a rear-facing camera or via a message received in acommunication session (e.g., a chat interface). The second video orimage may depict the second person performing a second pose or motionand wearing a second upper-body garment that is different from the firstpose or motion and first upper-body garment worn by the first persondepicted in the first image or video. In some examples, the motion andappearance transfer system 224 can receive the second video or image ofthe second person from a client device 102 of the second person in realtime instead of capturing the second image or video using the clientdevice 102 of the first person. For example, the motion and appearancetransfer system 224 can enable a first person to capture the secondimage or video of the second user by pointing an image capture device(e.g., rear-facing or front-facing camera) of the client device 102 ofthe first person towards a direction of the second person. In anotherexample, the motion and appearance transfer system 224 can enable thesecond person to capture the second image or video of the second personusing the client device 102 of the second person and to send thecaptured second image or video from the client device 102 of the secondperson to the client device 102 of the first person via a communicationsession.

The motion and appearance transfer system 224 applies an appearanceextraction module 512 to extract appearance features of the secondperson from the second image or video that depicts the second person. Insome cases, the appearance extraction module 512 extracts the appearancefeatures and motion features of the second person from the second imageor video that depicts the second person simultaneously but only retainsthe appearance features. The motion and appearance transfer system 224also extracts the motion features of the first person from the firstimage or video that depicts the first person by applying the motionextraction module 514 to the first image or video. The motion extractionmodule 514 can extract the appearance features and motion features ofthe first person from the first image or video that depicts the firstperson simultaneously but only retains the motion features. The motionand appearance features can be extracted by applying the first andsecond images or videos to a feature extraction network (e.g., a firstneural network or first machine learning technique). The first andsecond images or videos can be applied to the first neural network inparallel or sequentially to extract the appearance and/or motionfeatures from the first and second images or videos. Namely, theappearance extraction module 512 and the motion extraction module 514can be implemented by the first neural network or machine learningtechnique.

The motion and appearance transfer system 224 can then render a newimage or video that depicts the second person in the pose of the firstperson but wearing the second upper-body garment. In one example, themotion and appearance transfer system 224 applies to an image generationsystem (e.g., a second neural network implemented by the image renderingmodule 518) the appearance features of the second person extracted fromthe second image or video together with the motion features of the firstperson extracted from the first image or video. The image renderingmodule 518 can render a third image or video that depicts the secondperson in the pose or motion of the first person based on the appearanceand motion features received from the first machine learning techniquemodule. The image rendering module 518 can then apply a further neuralnetwork implemented by an upper-body segmentation module 515 to generatean upper-body segmentation of the upper-body garment worn by the secondperson that is now in the pose of the first person. Namely, theupper-body segmentation module 515 can be applied to the third image orvideo that depicts the second person in the pose or motion of the firstperson.

After generating the upper-body segmentation, an upper-body mask can beapplied to the third image or video to obtain pixel values and positionsof the upper-body garment worn by the second person. The image renderingmodule 518 can then render a fourth new image or video using theappearance and motion features of the first video that depicts the firstperson wearing the upper-body garment worn by the second person andusing the obtained pixel values and positions of the upper-body garmentworn by the second person. Specifically, the image rendering module 518can replace the pixel values and positions of the first video with thepixel values and positions obtained using the mask applied to the thirdvideo. This results in a fourth image or video that depicts the firstperson wearing the second upper-body garment worn by the second persondepicted in the second video instead of wearing the first upper-bodygarment. In some examples, the image rendering module 518 can implementa single neural network that is trained to render a new image or videothat replaces an upper-body garment worn by a person in a first videowith a different upper-body garment worn by another person in a secondvideo. In this case, the image rendering module 518 can render only onenew image or video based on receiving the motion and appearance featuresextracted from a first video depicting a first person wearing a firstupper-body garment and a second video depicting a second person wearinga second upper-body garment.

In some examples, the upper-body mask can be applied to the third imageor video to extract pixel values and positions of the upper-body garmentworn by the second person. The image rendering module 518 can obtainappearance features of the first video to determine lighting conditionsof the first video. The lighting conditions can be determined for aregion of the first video that depicts the upper-body garment worn bythe first person. The image rendering module 518 can modify one or morecolors of the extracted pixel values and positions of the upper-bodygarment worn by the second person based on the lighting conditions. Forexample, the image rendering module 518 can determine that a rightsleeve portion depicted in the first video has lighting conditions thatare brighter than a left sleeve portion depicted in the first video. Inresponse, the image rendering module 518 can increase a brightness,luminance, and/or intensity value of the extracted pixel values of theupper-body garment worn by the second person corresponding to the rightsleeve. Similarly, the image rendering module 518 can decrease abrightness, luminance, and/or intensity value of the extracted pixelvalues of the upper-body garment worn by the second person correspondingto the left sleeve. This preserves the lighting conditions of the firstvideo by modifying the lighting conditions of the garment depicted inthe second video to match the lighting conditions of the first video.

The image rendering module 518 can then render a fourth new image orvideo using the appearance and motion features of the first video thatdepicts the first person wearing the upper-body garment worn by thesecond person and using the obtained pixel values and positions of theupper-body garment worn by the second person which have been adjustedbased on the lighting conditions of the first video. Specifically, theimage rendering module 518 can replace the pixel values and positions ofthe first video with the pixel values and positions obtained using themask applied to the third video and which have been adjusted in color tomatch the lighting conditions of the first video. This results in afourth image or video that depicts the first person wearing the secondupper-body garment worn by the second person depicted in the secondvideo instead of wearing the first upper-body garment.

During training, the motion and appearance transfer system 224 receivesa set of training images or videos from training data 501. The motionand appearance transfer system 224 applies one or more machine learningtechniques using the first and second machine learning technique moduleson the given set of training images or videos. The first and secondmachine learning technique modules extract one or more features from thegiven set of training images or videos to render an estimated image orvideo that depicts a person having a certain appearance that matches anappearance of another person (e.g., the person’s upper-body garments inthe estimated image or video can be modified to match the upper-bodygarments worn by another person). The first and second machine learningtechnique modules can be trained end-to-end until a stopping criterionis met.

In an example, to train the first and second machine learning techniquesof the motion and appearance transfer system 224, the motion andappearance transfer system 224 obtains a first pair of training imagesor videos. The first pair of training images or videos include a firsttraining image or video that depicts a given person wearing a particularupper-body article of clothing and performing a first motion or pose.The first pair of training images or videos include a second trainingimage or video that depicts the same given person wearing a differentupper-body article of clothing and performing a second motion or pose.In some cases, the second training image or video depicts the same givenperson wearing the same upper-body article of clothing and performing asecond motion or pose.

The first training image or video is applied to the first machinelearning technique (e.g., the motion extraction module 514) to extractone or more motion features from the first training image or video. Inan example, the first machine learning technique extracts the appearanceand motion features simultaneously but the motion extraction module 514only retains the motion features of the first training image or video.The motion or pose features can include a 2D or 3D whole or upper-bodysegmentation, an upper-body mesh, 3D pose information and/or a set ofdense keypoints.

The second training image or video is applied to the first machinelearning technique (e.g., the appearance extraction module 512) toextract one or more appearance features from the second training imageor video. In an example, the first machine learning technique extractsthe appearance and motion features simultaneously but the appearanceextraction module 512 only retains the appearance features of the secondtraining image or video. The appearance features can include a 2D or 3Dtexture and pixel color information and upper-body segmentation andupper-body article of clothing (garment) segmentation and/or a set ofdense keypoints.

The appearance features extracted from the second training image orvideo and the motion features extracted from the first training image orvideo are applied to the image rendering module 518 (e.g., a secondmachine learning technique). The image rendering module 518 generates orestimates a new image or video in which the given person depicted in thefirst training image or video is depicted as wearing the particularupper-body article of clothing as the given person depicted in thesecond training image or video. Namely, the second machine learningtechnique is trained to generate an image or video that retains themotion of a person in a first image but that has an appearance in whichan upper-body garment is replaced to copy an upper-body garment worn byanother person in a second image or video. The image rendering module518 can adjust colors (pixel values, luminance, intensity, and so forth)of the upper-body garment worn by the person in the second image orvideo based on lighting conditions or properties of the first image orvideo.

In one example, in order to generate the image or video that retains themotion of a person in a first image but that has an appearance in whichan upper-body garment is replaced to copy an upper-body garment worn byanother person in a second image or video, the image rendering module518 generates or renders one or more intermediate images or videos.Specifically, the image rendering module 518 can generate anintermediate image or video by applying motion features of the givenperson depicted in the first training image or video and appearancefeatures of the given person depicted in the second training image orvideo to a neural network. The neural network renders an intermediateimage or video based on the received motion and appearance features todepict the given person in the second training image or video performinga motion or pose of the given person in the first training image orvideo. In this way, the intermediate image can represent the givenperson in the second training image or video as wearing the sameupper-body garment but in a different pose or motion corresponding tothe pose or motion of the given person in the first training image orvideo.

The intermediate image or video is applied to an upper-body segmentationmodule 515. The upper-body segmentation module 515 implements a neuralnetwork that is trained to generate an upper-body segmentation of thearticles of clothing worn by a person depicted in an image or video. Insome cases, the upper-body segmentation module 515 is implemented by thefirst machine learning technique. In these cases, the upper-bodysegmentation is provided as part of the appearance features extracted bythe first machine learning technique. In some cases, the upper-bodysegmentation module 515 is implemented by the second machine learningtechnique. The upper-body segmentation module 515 outputs thesegmentation that identifies the borders or regions of the intermediateimage of the upper-body outfit or upper-body garment worn by the givenperson. The upper-body segmentation module 515 can also apply a maskbased on the upper-body segmentation to obtain a set of pixels (valuesand positions) representing the upper-body garment worn by the givenperson in the intermediate image. These obtained set of pixels are usedby the image rendering module 518 to replace the corresponding set ofpixels in the first training image or video of the given person. Thisresults in the new image or video that retains the motion of the personin the first training image but that has an appearance in which anupper-body garment is replaced to copy an upper-body garment worn by thegiven person in the second training image or video.

The motion and appearance transfer system 224 compares the generated orestimated new image or video with the second training image or video tocompute a deviation. Specifically, during training the motion andappearance transfer system 224 renders an image or video thatapproximates the second training image or video in which the persondepicted in the first training image is wearing a different upper-bodygarment. Namely, the motion and appearance transfer system 224 adaptsthe appearance and motion of the same person that is depicted in thefirst training image wearing one upper-body garment to match a differentupper-body garment of the same person that is depicted in the secondtraining image. Based on how close the rendered image or video is to thesecond training image or video, the motion and appearance transfersystem 224 makes a determination to complete training.

In an example, the motion and appearance transfer system 224 updates oneor more parameters of the first and/or second machine learningtechniques based on the computed deviation. The motion and appearancetransfer system 224 determines if the computed deviation is within athreshold error or if a certain number of iterations or epochs have beenperformed to determine if a stopping criterion is met. In response todetermining that the stopping criterion has been met, the motion andappearance transfer system 224 completes training and the parameters andcoefficients of the machine learning techniques are stored in thetrained machine learning technique(s) 307. In response to determiningthat the stopping criterion has not been met, the motion and appearancetransfer system 224 obtains a second pair of training images or videosthat depict the same person wearing the different upper-body articles ofclothing and performing different motions or poses. The motion andappearance transfer system 224 iterates through the above trainingprocess to render a new image in which upper-body garments worn by theperson in one of the training images or videos is modified to mirror orcopy the upper-body garments worn by the person in a second of thetraining images or videos. Parameters of the first and/or second machinelearning techniques are again updated and a deviation is computed todetermine whether a stopping criterion is met.

In this way, the motion and appearance transfer system 224 trains themachine learning technique modules to establish a relationship betweenthe appearance and motion of a person in a first image or video, theappearance and motion of a person in a second image or video, and arendered image or video of the person in the first image or videowearing a different upper-body garment corresponding to the upper-bodygarment worn by the second person in the second image or video.

After the motion and appearance transfer system 224 trains the first andsecond machine learning technique modules, the motion and appearancetransfer system 224 can receive a new pair of images. The motion andappearance transfer system 224 can generate a new image in which theappearance of a person in one of the new pair of images is modified tohave the upper-body garment being worn by the person changed to match anupper-body garment worn by the person in a second one of the new pair ofimages. Particularly, the motion and appearance transfer system 224 canreceive input from a first person that selects a first image or videothat depicts the first person performing a first motion and wearing afirst upper-body garment. The AR effect module 519 can receive inputfrom the first person that activates the AR experience in which anupper-body garment of another person in a target image or video is usedto modify the first upper-body garment worn by the first person in thefirst image or video. In response, the motion and appearance transfersystem 224 receives the target image or video that depicts a secondperson wearing a second upper-body garment and performing a secondmotion or pose. The motion and appearance transfer system 224 canextract the motion features of the first person depicted in the firstimage or video and can extract the appearance features of the secondperson depicted in the target image or video. The motion and appearancetransfer system 224 applies the appearance features and the motionfeatures to the second neural network (e.g., an image rendering module518).

The image rendering module 518 renders an intermediate image or videothat depicts the second person wearing the second upper-body garment andperforming the first motion or pose of the first person. In someexamples, the image rendering module 518 renders the intermediate imageor video based on 3D body tracking information obtained from the 3D bodytracking module 513 and an upper-body segmentation obtained from theupper-body segmentation module 515. The 3D body tracking module 513 cancontinuously track the movement of the first person depicted in thefirst image or video to enable the image rendering module 518 tocontinuously and in real time update the motion or pose of the secondperson depicted in the second image or video. The upper-bodysegmentation module 515 can continuously generate an upper-bodysegmentation of the first person depicted in the first image or video toenable the image rendering module 518 to continuously and in real timeupdate the motion and appearance of the second person in theintermediate image or video.

The image rendering module 518 applies an upper-body garmentsegmentation to the intermediate image or video to generate anupper-body garment segmentation. Namely, after the intermediate image orvideo is generated to depict the second person wearing the secondupper-body garment in the pose of the first person depicted in the firstimage or video, the upper-body garment segmentation of the second personcan be generated. A mask is then applied to the upper-body garmentsegmentation to obtain the pixels corresponding to the upper-bodygarment worn by the second person. The obtained pixels are then used bythe image rendering module 518 to render a new image or video based onthe first image or video in which the first person is depicted aswearing the second upper-body garment worn by the second person in thesecond image or video. In one example, the image rendering module 518copies and pastes the obtained pixels of the second upper-body garmentfrom the mask generated based on the intermediate image or video tocorresponding pixel positions of the first video that depicts the firstperson wearing the first upper-body garment. This results in a depictionof the first person wearing the second upper-body garment. In somecases, before pasting the obtained pixels, the image rendering module518 modifies the pixel values of one or more portions based on lightingconditions of the first image or video.

FIG. 6 is a diagrammatic representation of outputs of the motion andappearance transfer system 224, in accordance with some examples.Specifically, FIG. 6 shows a 3D body mesh 600 extracted by the firstmachine learning technique module (e.g., the appearance extractionmodule 512 and the motion extraction module 514). The 3D body mesh 600can be associated with one or more appearance and/or motion featuresthat are also extracted by the first machine learning technique module.In one example, the one or more appearance and/or motion features caninclude a three-dimensional (3D) pose of a person depicted in one of theimages or videos, an upper-body segmentation, a set of dense keypoints,texture, color, and/or an upper-body garment segmentation of the persondepicted in the images or videos.

In one example, as shown in diagram 700 of FIG. 7 , motion andappearance transfer system 224 can receive a first image or video 710,such as from a rear-facing camera of a client device 102 of a firstperson. The first image or video 710 depicts a second person performinga first motion wearing a first upper-body garment (e.g., a shirt). Themotion and appearance transfer system 224 can receive a second image orvideo 720, such as from a front-facing (or rear-facing) camera of theclient device 102 of the first person. The second image or video 720 candepict the first person performing a second motion and wearing a secondupper-body garment (e.g., a second shirt).

The first image or video 710 can be received at the same time as thesecond image or video 720. In this case, the front-facing andrear-facing cameras of the client device 102 of the first person can beactive at the same time. The display of the client device 102 candisplay the image or video captured by the front-facing camera in oneregion of the screen and the image or video captured by the rear-facingcamera in a second region of the screen. The regions can overlap suchthat the image or video captured by the front-facing camera is displayedon top of the image or video captured by the rear-facing camera, such asin a top or bottom corner.

In one example, the first image or video 710 and second image or video720 can be received and captured in real time together. In one example,the second image or video 720 can be initially captured and receivedbefore capturing the first image or video 710. In one example, the firstimage or video 710 can be initially captured and received beforecapturing the second image or video 720.

The motion and appearance transfer system 224 can apply a first instance730 of the first machine learning technique to the first image or video710. The first instance 730 of the first machine learning technique canextract appearance features from the first image or video 710. Theappearance features represent how the second person looks in the firstimage or video 710 including a texture and color of the second personand the upper-body garment segmentation of the second person. The motionand appearance transfer system 224 can apply a second instance 731 ofthe first machine learning technique to the second image or video 720.The second instance 731 of the first machine learning technique canextract motion features from the second image or video 720. The motionfeatures represent how the first person moves in the second image orvideo 720 including a 3D body pose and 3D upper-body segmentation of thefirst person.

The motion and appearance transfer system 224 applies, to a secondmachine learning technique 740, the appearance and motion featuresextracted from the first and second images or videos 710 and 720. Thesecond machine learning technique 740 renders a new image or video 750that depicts the first person having an appearance of the first personand wearing the second upper-body garment worn by the second persondepicted in the first image or video 710. The new image or video 750 canbe displayed together with the first and second images or videos 710 and720 on the same client device 102 so the first person can see thechanges to the first person’s upper-body garments in real time.

FIG. 8 is a flowchart of a process 800 performed by the motion andappearance transfer system 224, in accordance with some examples.Although the flowchart can describe the operations as a sequentialprocess, many of the operations can be performed in parallel orconcurrently. In addition, the order of the operations may bere-arranged. A process is terminated when its operations are completed.A process may correspond to a method, a procedure, and the like. Thesteps of methods may be performed in whole or in part, may be performedin conjunction with some or all of the steps in other methods, and maybe performed by any number of different systems or any portion thereof,such as a processor included in any of the systems.

At operation 801, the motion and appearance transfer system 224 (e.g., aclient device 102 or a server) receives a first video that includes adepiction of a first person wearing a first upper-body garment in afirst pose, as discussed above. For example, a second image or video 720can be received from a front-facing camera of a client device 102 of afirst person that depicts the first person wearing a first upper-bodygarment and in a first pose.

At operation 802, the motion and appearance transfer system 224 obtainsa second video that includes a depiction of a second person wearing asecond upper-body garment in a second pose, as discussed above. Forexample, a first image or video 710 can be received from a rear-facingcamera of a client device 102 of the first person that depicts a secondperson wearing a second upper-body garment and in a second pose.

At operation 803, the motion and appearance transfer system 224 modifiesa pose of the second person depicted in the second video from the secondpose to the first pose to match the first pose of the first persondepicted in the first video, as discussed above. For example, the secondmachine learning technique 740 can generate or render an intermediateimage or video in which the pose of the second person is modified tomirror or copy the pose of the first person depicted in the second imageor video 720. The intermediate image or video can be generated orrendered based on motion and appearance features extracted by the firstmachine learning technique instances 730 and 731 from the first andsecond images or videos 710 and 720.

At operation 804, the motion and appearance transfer system 224generates an upper-body segmentation of the second upper-body garmentwhich the second person is wearing in the second video in the modifiedpose, as discussed above. For example, the second machine learningtechnique 740 can apply an upper-body segmentation to the intermediateimage or video to generate an upper-body segmentation of the secondupper-body garment worn by the second person that represents the bordersof the second upper-body garment.

At operation 805, the motion and appearance transfer system 224 replacesthe first upper-body garment worn by the first person in the first videowith the second upper-body garment based on the upper-body segmentation,as discussed above. For example, the second machine learning technique740 can obtain the pixels from the segmentation of the second upper-bodygarment to modify the corresponding pixel values of the second image orvideo 720. This results in a new image or video that depicts the firstperson wearing the second upper-body garment instead of the firstupper-body garment.

Machine Architecture

FIG. 9 is a diagrammatic representation of the machine 900 within whichinstructions 908 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 900 to performany one or more of the methodologies discussed herein may be executed.For example, the instructions 908 may cause the machine 900 to executeany one or more of the methods described herein. The instructions 908transform the general, non-programmed machine 900 into a particularmachine 900 programmed to carry out the described and illustratedfunctions in the manner described. The machine 900 may operate as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 900 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 900 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smartphone, a mobile device, a wearable device(e.g., a smartwatch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 908, sequentially or otherwise, that specify actions to betaken by the machine 900. Further, while only a single machine 900 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 908 to perform any one or more of the methodologiesdiscussed herein. The machine 900, for example, may comprise the clientdevice 102 or any one of a number of server devices forming part of themessaging server system 108. In some examples, the machine 900 may alsocomprise both client and server systems, with certain operations of aparticular method or algorithm being performed on the server-side andwith certain operations of the particular method or algorithm beingperformed on the client-side.

The machine 900 may include processors 902, memory 904, and input/output(I/O) components 938, which may be configured to communicate with eachother via a bus 940. In an example, the processors 902 (e.g., a CentralProcessing Unit (CPU), a Reduced Instruction Set Computing (RISC)Processor, a Complex Instruction Set Computing (CISC) Processor, aGraphics Processing Unit (GPU), a Digital Signal Processor (DSP), anApplication Specific Integrated Circuit (ASIC), a Radio-FrequencyIntegrated Circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 906 and aprocessor 910 that execute the instructions 908. The term “processor” isintended to include multi-core processors that may comprise two or moreindependent processors (sometimes referred to as “cores”) that mayexecute instructions contemporaneously. Although FIG. 9 shows multipleprocessors 902, the machine 900 may include a single processor with asingle-core, a single processor with multiple cores (e.g., a multi-coreprocessor), multiple processors with a single core, multiple processorswith multiples cores, or any combination thereof.

The memory 904 includes a main memory 912, a static memory 914, and astorage unit 916, all accessible to the processors 902 via the bus 940.The main memory 904, the static memory 914, and the storage unit 916store the instructions 908 embodying any one or more of themethodologies or functions described herein. The instructions 908 mayalso reside, completely or partially, within the main memory 912, withinthe static memory 914, within a machine-readable medium within thestorage unit 916, within at least one of the processors 902 (e.g.,within the processor’s cache memory), or any suitable combinationthereof, during execution thereof by the machine 900.

The I/O components 938 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 938 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 938 mayinclude many other components that are not shown in FIG. 9 . In variousexamples, the I/O components 938 may include user output components 924and user input components 926. The user output components 924 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 926 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 938 may include biometriccomponents 928, motion components 930, environmental components 932, orposition components 934, among a wide array of other components. Forexample, the biometric components 928 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 930 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope).

The environmental components 932 include, for example, one or morecameras (with still image/photograph and video capabilities),illumination sensor components (e.g., photometer), temperature sensorcomponents (e.g., one or more thermometers that detect ambienttemperature), humidity sensor components, pressure sensor components(e.g., barometer), acoustic sensor components (e.g., one or moremicrophones that detect background noise), proximity sensor components(e.g., infrared sensors that detect nearby objects), gas sensors (e.g.,gas detection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the client device 102 may have a camera systemcomprising, for example, front cameras on a front surface of the clientdevice 102 and rear cameras on a rear surface of the client device 102.The front cameras may, for example, be used to capture still images andvideo of a user of the client device 102 (e.g., “selfies”), which maythen be augmented with augmentation data (e.g., filters) describedabove. The rear cameras may, for example, be used to capture stillimages and videos in a more traditional camera mode, with these imagessimilarly being augmented with augmentation data. In addition to frontand rear cameras, the client device 102 may also include a 360° camerafor capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad, or penta rear camera configurations on the front andrear sides of the client device 102. These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera, and a depth sensor, for example.

The position components 934 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 938 further include communication components 936operable to couple the machine 900 to a network 920 or devices 922 viarespective coupling or connections. For example, the communicationcomponents 936 may include a network interface component or anothersuitable device to interface with the network 920. In further examples,the communication components 936 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 922 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 936 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 936 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components936, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 912, static memory 914, andmemory of the processors 902) and storage unit 916 may store one or moresets of instructions and data structures (e.g., software) embodying orused by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 908), when executedby processors 902, cause various operations to implement the disclosedexamples.

The instructions 908 may be transmitted or received over the network920, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components936) and using any one of several well-known transfer protocols (e.g.,HTTP). Similarly, the instructions 908 may be transmitted or receivedusing a transmission medium via a coupling (e.g., a peer-to-peercoupling) to the devices 922.

Software Architecture

FIG. 10 is a block diagram 1000 illustrating a software architecture1004, which can be installed on any one or more of the devices describedherein. The software architecture 1004 is supported by hardware such asa machine 1002 that includes processors 1020, memory 1026, and I/Ocomponents 1038. In this example, the software architecture 1004 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 1004 includes layerssuch as an operating system 1012, libraries 1010, frameworks 1008, andapplications. Operationally, the applications 1006 invoke API calls 1050through the software stack and receive messages 1052 in response to theAPI calls 1050.

The operating system 1012 manages hardware resources and provides commonservices. The operating system 1012 includes, for example, a kernel1014, services 1016, and drivers 1022. The kernel 1014 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 1014 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 1016 canprovide other common services for the other software layers. The drivers1022 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1022 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., USB drivers), WI-FI®drivers, audio drivers, power management drivers, and so forth.

The libraries 1010 provide a common low-level infrastructure used byapplications 1006. The libraries 1010 can include system libraries 1018(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 1010 can include APIlibraries 1024 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in 2D and 3) in a graphiccontent on a display), database libraries (e.g., SQLite to providevarious relational database functions), web libraries (e.g., WebKit toprovide web browsing functionality), and the like. The libraries 1010can also include a wide variety of other libraries 1028 to provide manyother APIs to the applications 1006.

The frameworks 1008 provide a common high-level infrastructure that isused by the applications 1006. For example, the frameworks 1008 providevarious graphical user interface functions, high-level resourcemanagement, and high-level location services. The frameworks 1008 canprovide a broad spectrum of other APIs that can be used by theapplications 1006, some of which may be specific to a particularoperating system or platform.

In an example, the applications 1006 may include a home application1036, a contacts application 1030, a browser application 1032, a bookreader application 1034, a location application 1042, a mediaapplication 1044, a messaging application 1046, a game application 1048,and a broad assortment of other applications such as an externalapplication 1040. The applications 1006 are programs that executefunctions defined in the programs. Various programming languages can beemployed to create one or more of the applications 1006, structured in avariety of manners, such as object-oriented programming languages (e.g.,Objective-C, Java, or C++) or procedural programming languages (e.g., Cor assembly language). In a specific example, the external application1040 (e.g., an application developed using the ANDROID™ or IOS™ SDK byan entity other than the vendor of the particular platform) may bemobile software running on a mobile operating system such as IOS™,ANDROID™, WINDOWS® Phone, or another mobile operating system. In thisexample, the external application 1040 can invoke the API calls 1050provided by the operating system 1012 to facilitate functionalitydescribed herein.

GLOSSARY

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, PDAs, smartphones,tablets, ultrabooks, netbooks, laptops, multi-processor systems,microprocessor-based or programmable consumer electronics, gameconsoles, set-top boxes, or any other communication device that a usermay use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1xRTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions.

Components may constitute either software components (e.g., codeembodied on a machine-readable medium) or hardware components. A“hardware component” is a tangible unit capable of performing certainoperations and may be configured or arranged in a certain physicalmanner. In various examples, one or more computer systems (e.g., astandalone computer system, a client computer system, or a servercomputer system) or one or more hardware components of a computer system(e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwarecomponent that operates to perform certain operations as describedherein.

A hardware component may also be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware component may include dedicated circuitry or logic that ispermanently configured to perform certain operations. A hardwarecomponent may be a special-purpose processor, such as afield-programmable gate array (FPGA) or an ASIC. A hardware componentmay also include programmable logic or circuitry that is temporarilyconfigured by software to perform certain operations. For example, ahardware component may include software executed by a general-purposeprocessor or other programmable processor. Once configured by suchsoftware, hardware components become specific machines (or specificcomponents of a machine) uniquely tailored to perform the configuredfunctions and are no longer general-purpose processors. It will beappreciated that the decision to implement a hardware componentmechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software), may bedriven by cost and time considerations. Accordingly, the phrase“hardware component”(or “hardware-implemented component”) should beunderstood to encompass a tangible entity, be that an entity that isphysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein.

Considering examples in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inexamples in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some examples, the processors or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexamples, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure.

“Ephemeral message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo, and the like. The access time for the ephemeral message may beset by the message sender. Alternatively, the access time may be adefault setting or a setting specified by the recipient. Regardless ofthe setting technique, the message is transitory.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,”“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

Changes and modifications may be made to the disclosed examples withoutdeparting from the scope of the present disclosure. These and otherchanges or modifications are intended to be included within the scope ofthe present disclosure, as expressed in the following claims.

What is claimed is:
 1. A method comprising: receiving, by one or moreprocessors, a first video that includes a depiction of a first personwearing a first upper-body garment in a first pose; obtaining a secondvideo that includes a depiction of a second person wearing a secondupper-body garment in a second pose; modifying the second pose of thesecond person depicted in the second video from the second pose to thefirst pose to match the first pose of the first person depicted in thefirst video; generating an upper-body segmentation of the secondupper-body garment which the second person is wearing in the secondvideo in the modified pose; and replacing the first upper-body garmentworn by the first person in the first video with the second upper-bodygarment based on the upper-body segmentation.
 2. The method of claim 1,wherein replacing the first upper-body garment worn by the first personcomprises: extracting appearance and motion data associated with thefirst person from the first video; extracting appearance and motion dataassociated with the second person from the second video; modifying themotion data associated with the second person to match the motion dataassociated with the first person; applying an upper-body mask to theappearance data associated with the second person after modifying themotion data associated with the second person to obtain pixelscorresponding to the second upper-body garment; and generating a thirdvideo that includes a depiction of the first person wearing the secondupper-body garment instead of the first garment.
 3. The method of claim2, further comprising: applying the upper-body mask to the appearancedata associated with the first person to identify a set of targetpixels; and copying the obtained pixels corresponding to the secondupper-body garment to the set of target pixels.
 4. The method of claim1, wherein modifying the pose of the second person comprises: extractingan appearance data associated with the second person from the secondvideo; extracting motion data associated with the first person from thefirst video; and generating a third video that includes a depiction ofthe second person having the appearance data associated with the secondperson and the motion of the first person.
 5. The method of claim 4,further comprising: applying an upper-body mask to the third video toobtain a set of pixels corresponding to the second upper-body garment;and copying pixels within the upper-body mask to corresponding pixelpositions in the first video to generate a fourth video that includes adepiction of the first person wearing the second upper-body garmentinstead of the first upper-body garment.
 6. The method of claim 2,wherein the appearance data associated with the second person comprisesa three-dimensional (3D) pose, an upper-body body segmentation, a set ofdense keypoints, texture, color, texture, and an upper-body garmentsegmentation.
 7. The method of claim 2, wherein extracting theappearance and motion data associated with the first and second personscomprises applying a first machine learning technique to the first andsecond videos, the first machine learning technique being trained toestimate appearance and motion data associated with an input video. 8.The method of claim 7, wherein the first machine learning technique istrained to generate a three-dimensional (3D) pose of a given real-worldobject depicted in the input video, an upper-body segmentation of thegiven real-world object, a set of dense keypoints of the givenreal-world object, texture, color, and an upper-body garmentsegmentation of the given real-world object simultaneously.
 9. Themethod of claim 8, further comprising: applying the motion dataassociated with the first person extracted from the first video and theappearance data associated with the second person extracted from thesecond video to a second machine learning technique, the second machinelearning technique being trained to apply the motion of a third personto an appearance of a fourth person and to render a new video thatdepicts the third person wearing an upper-body garment of the fourthperson.
 10. The method of claim 9, further comprising training the firstand second machine learning techniques by iterating through a sequenceof operations comprising: receiving a first training video that depictsa training person in a first training pose and wearing a first trainingupper-body garment; receiving a second training video that depicts thetraining person in a second training pose and wearing a second trainingupper-body garment; applying the first machine learning technique to thefirst and second training videos to generate first and second sets ofestimated appearance and motion data associated with the trainingperson; and applying the first and second sets of estimated appearanceand motion data associated with the training person to the secondmachine learning technique to generate a depiction of the trainingperson wearing the second training upper-body garment.
 11. The method ofclaim 10, further comprising: computing a deviation between thegenerated depiction of the training person wearing the second trainingupper-body garment and the second training video that depicts thetraining person wearing the second training upper-body garment; andupdating one or more parameters of the first and second machine learningtechniques based on the computed deviation.
 12. The method of claim 10,wherein the first machine learning technique comprises an appearance andmotion data extraction module and the second machine learning techniquecomprises an image generator module.
 13. The method of claim 1, furthercomprising: capturing the second video that depicts the second personusing an image capture device of a client device of the first person;and after capturing the second video, capturing the first video thatdepicts the first person using the image capture device, wherein a thirdvideo is generated in response to capturing the first video aftercapturing the second video, the third video depicting the first personwearing the second upper-body garment being worn by the second person.14. The method of claim 13, wherein the second video is captured using arear-facing camera of the client device and the first video is capturedusing a front-facing camera of the client device.
 15. The method ofclaim 1, wherein the first and second videos are captured simultaneouslyusing different cameras of a client device.
 16. The method of claim 1,wherein a third video depicting a change in appearance of the firstperson is generated in real-time as the first and second videos arebeing captured.
 17. The method of claim 1, wherein replacing the firstupper-body garment worn by the first person in the first videocomprises: obtaining lighting conditions associated with the firstvideo; extracting the second upper-body garment in the modified pose ofthe second person based on the upper-body segmentation; modifying one ormore colors of the extracted second upper-body garment based on thelighting conditions associated with the first video; and applying theextracted second upper-body garment with the modified one or more colorsto an appearance data associated with the first person in the firstvideo.
 18. A system comprising: a processor; and a memory componenthaving instructions stored thereon that, when executed by the processor,cause the processor to perform operations comprising: receiving a firstvideo that includes a depiction of a first person wearing a firstupper-body garment in a first pose; obtaining a second video thatincludes a depiction of a second person wearing a second upper-bodygarment in a second pose; modifying the second pose of the second persondepicted in the second video from the second pose to the first pose tomatch the first pose of the first person depicted in the first video;generating an upper-body segmentation of the second upper-body garmentwhich the second person is wearing in the second video in the modifiedpose; and replacing the first upper-body garment worn by the firstperson in the first video with the second upper-body garment based onthe upper-body segmentation.
 19. The system of claim 18, whereinreplacing the first upper-body garment worn by the first person in thefirst video comprises: obtaining lighting conditions associated with thefirst video; extracting the second upper-body garment in the modifiedpose of the second person based on the upper-body segmentation;modifying one or more colors of the extracted second upper-body garmentbased on the lighting conditions associated with the first video; andapplying the extracted second upper-body garment with the modified oneor more colors to an appearance data associated with the first person inthe first video.
 20. A non-transitory computer-readable storage mediumhaving stored thereon instructions that, when executed by a processor,cause the processor to perform operations comprising: receiving a firstvideo that includes a depiction of a first person wearing a firstupper-body garment in a first pose; obtaining a second video thatincludes a depiction of a second person wearing a second upper-bodygarment in a second pose; modifying the second pose of the second persondepicted in the second video from the second pose to the first pose tomatch the first pose of the first person depicted in the first video;generating an upper-body segmentation of the second upper-body garmentwhich the second person is wearing in the second video in the modifiedpose; and replacing the first upper-body garment worn by the firstperson in the first video with the second upper-body garment based onthe upper-body segmentation.